Time Consumption Research Articles

Development of optimal L-PBF process parameters using an accelerated discrete element simulation framework

Laser Powder Bed Fusion (L-PBF) has immense potential for the production of complex, lightweight, and high-performance components. The traditional optimization of process parameters is costly and time-intensive, due to reliance on experimental approaches. Current numerical analyses often model single-line scans, while it is necessary to model multiple fully scanned layers to optimize for bulk material quality. Here, we introduce a novel approach utilizing discrete element simulations with a ray tracing-modeled laser heat source. Our approach significantly reduces the cost and time consumption compared to conventional optimization methods. GPU acceleration enables efficient simulation of multiple layers, resulting in parameters optimized for bulk material. In a case study, parameters were optimized for AlSi10Mg in just 5 days, a process that would have taken over 8 months without GPU acceleration. Experimental validation affirms the quality of the optimized process parameters, achieving an optical density of 99.91%.Graphical Optimization using the accelerated simulation yielded an optimized parameter set within 5 days. This resulted in apart with an optical density of 99.91%.

USV Path Planning in a Hybrid Map Using a Genetic Algorithm with a Feedback Mechanism

Unmanned surface vehicles (USVs) often operate in real-world environments with long voyage distances and complex routes. The use of a single-grid map model presents challenges, such as the high computational costs for high-resolution maps and loss of environmental information for low-resolution maps. This article proposes an environmental modeling method using a hybrid map that combines topology units and grids. The approach involves calibrating key nodes based on the watershed skeleton line, constructing a topology map using these nodes, decomposing the original map into unit maps, converting each unit map into a grid map, and creating a hybrid map environment model that comprises topology maps, unit map sets, and grid map sets. Then, the article introduces an improved genetic algorithm, called Genetic Algorithm with Feedback (FGA), to address path planning in hybrid maps. Experimental results demonstrate that FGA has better computational efficiency than other algorithms in similar experimental environments. In hybrid maps, path planning with FGA reduces the path lengths and time consumption, and the paths are more logical, smooth, and continuous. These findings contribute to enhancing the quality of path planning and the practical value of USVs.

Meta-learning based blind image super-resolution approach to different degradations

Although recent studies on blind single image super-resolution (SISR) have achieved significant success, most of them typically require supervised training on synthetic low resolution (LR)-high resolution (HR) paired images. This leads to re-training necessity for different degradations and restricted applications in real-world scenarios with unfavorable inputs. In this paper, we propose an unsupervised blind SISR method with input underlying different degradations, named different degradations blind super-resolution (DDSR). It formulates a Gaussian modeling on blur degradation and employs a meta-learning framework for solving different image degradations. Specifically, a neural network-based kernel generator is optimized by learning from random kernel samples, referred to as random kernel learning. This operation provides effective initialization for blur degradation optimization. At the same time, a meta-learning framework is proposed to resolve multiple degradation modelings on the basis of alternative optimization between blur degradation and image restoration, respectively. Differing from the pre-trained deep-learning methods, the proposed DDSR is implemented in a plug-and-play manner, and is capable of restoring HR image from unfavorable LR input with degradations such as partial coverage, noise addition, and darkening. Extensive simulations illustrate the superior performance of the proposed DDSR approach compared to the state-of-the-arts on public datasets with comparable memory load and time consumption, yet exhibiting better application flexibility and convenience, and significantly better generalization ability towards multiple degradations. Our code is available at https://github.com/XYLGroup/DDSR.

FSLEdge: An energy-aware edge intelligence framework based on Federated Split Learning for Industrial Internet of Things

Federated Learning (FL) enabled edge computing has been widely used in training complex deep learning models by coordinating various heterogeneous resources in Industrial Internet of Things (IIoT). However, the existing FL solutions may cause long training time or huge energy consumption for resource-constrained terminal or edge nodes. To address this issue, we propose an adaptive edge intelligence offloading framework based on Federated Split Learning (FSL) and Reinforcement Learning (RL), named FSLEdge, for IIoT systems. To begin with, a Split Learning (SL) approach was added to FL to reduce the training time and energy consumption by allowing local devices to offload some training layers into the edge servers while ensuring the protection of private data. Additionally, FSLEdge adopts RL to find the optimal split point to adaptively identify which is the best layers of the deep learning models should be offloaded into the edge servers with the goal of reducing the training time and energy consumption. Experimental studies are conducted under two different datasets: one public, CIFAR10, and one private, GridDataset, a transmission line fault detection dataset in smart grid domain. And the results show that the training time and energy consumption of the system by our proposed FSLEdge can be reduced by 10% and 21%, respectively, compared to the classical FL, without sacrificing accuracy.

Research on the construction method of virtual mapping digital twin system for substation

Abstract At present, there are problems in the process of fault location and cause analysis in the actual work of substations, such as long time consumption, high investment in manpower and material resources, and low returns. A Digital Twin (DT) system construction method that can be used for virtual mapping in substations is proposed to address this problem. Firstly, the design principles and overall structure of the substation DT system were formulated by comprehensively considering the needs of transient modeling, debugging, and verification calculations in the substation. Then, the construction method and process of the three-dimensional twin model of the primary system (PS) in the substation were analyzed by using SolidWorks and Unity3D, and the graphical modeling method and process of the general protection and secondary circuit logic modules were analyzed by using the Cybersim integrated simulation support system. Finally, a three-dimensional twin model of the PS and secondary system (SS) equipment inside the substation was constructed. The reliability of the proposed method was verified through simulation experiments, and the results showed that the fault identification accuracy, fault location accuracy, and fault cause judgment accuracy were the highest during bus faults, with 96.10%, 94.17%, and 92.93%, respectively. The capacitor had the lowest accuracy, with 94.86%, 92.96%, and 91.73%, respectively. The DT system can accurately achieve functions such as status viewing and alarm of the substation, precise positioning and navigation of equipment, and positioning of faulty and abnormal equipment.

Nutritional quality of fish oil extracted from selected freshwater fish species

There is increased consumer awareness of the nutritional importance of fish oil consumption in recent time, however there is paucity of information on the quality of oil obtained from freshwater fishes available in this study area. Nutritional quality of fish oil extracted from 9kg each of Chrysichthys nigrodigitatus, Synodontis membranaceous and Clarias gariepinus were investigated with the aim of providing baseline information on the nutritional quality of oil in the fish available around the study area. Wet rendering, soxhlet extraction and microwave assisted extraction methods were used. Data collected were subjected to appropriate statistical analysis using SPSS 6.0. version. S. membranaceous have the largest oil yield while C. nigrodigitatus have the lowest oil yield; wet rendering oil extraction method gave the highest oil yield. The physical quality characteristics of S. membranaceous was the best with the lowest melting point (28.73 ±0.39°C), specific gravity (0.11± 0.01) and refractive index (1.14 ±0.01) while C. gariepinus oil had the poorest physical quality characteristics. Oil of C. nigrodigitatus have better chemical qualities than the rest having the lowest saponification value (9.81±0.01 mg KOH/g), iodine value (1.59±0.01 gl/100g), acid value (1.12±0.01 mgKOH/g) and free fatty acid (0.56±0.01%) while C. gariepinus oil was the poorest. C. nigrodigitatus also have the highest polyunsaturated fatty acid (PUFA) (18.75), highest Ʃn-3 FA (23.45), highest DHA (Docosahexaenoic acid) (4.93), highest eicosapentaenoic acid EPA+DHA (7.8). The oils chemical qualities are in the order of C. nigrodigitatus> S. membranaceous> C. gariepinus and are of good quality. Oil obtained from C. nigrodigitatus is therefore recommended due to its good chemical qualities using wet rendering oil extraction method. Information provided in this study will guide industrialists and nutritionists on the best extraction method and fish that will give highest oil yield and best oil quality in the study area.

Time‐energy consumption optimal path‐constrained trajectory planning of excavator robotics

AbstractReasonable operation of each excavator joint under path constraints directly affects operation efficiency and energy consumption. Efficiency and energy consumption are considerably important performance indicators, particularly for large equipment, such as excavators. Therefore, we propose a quadratic trajectory optimization method based on time–energy consumption. With the pseudo‐path velocity, acceleration, and equivalent convex jerk as constraints, the interior‐point method was used to obtain the time–energy consumption optimal trajectory of an excavator. The time and energy consumption values for different weight coefficients were obtained, and the optimal trajectory planning of the excavator was achieved. Furthermore, the simulation results of the proposed method for lifting 70 kg of materials for a weight coefficient of 0.5 were compared with the result of second‐order cone programming. Experimental results showed that using proposed method to plan the motion of each joint reasonably and effectively reduced the pseudo‐path jerk, acceleration, and velocity peaks by 40.67%, 25.03%, and 12.54%, respectively, ensuring smooth excavator movement. It also improved the working efficiency of the excavator; reduced unnecessary energy consumption; and improved the stability, efficiency, and energy saving of the autonomous operation of the excavator.

AIfES: A Next-Generation Edge AI Framework.

Edge Artificial Intelligence (AI) relies on the integration of Machine Learning (ML) into even the smallest embedded devices, thus enabling local intelligence in real-world applications, e.g. for image or speech processing. Traditional Edge AI frameworks lack important aspects required to keep up with recent and upcoming ML innovations. These aspects include low flexibility concerning the target hardware and limited support for custom hardware accelerator integration. Artificial Intelligence for Embedded Systems Framework (AIfES) has the goal to overcome these challenges faced by traditional edge AI frameworks. In this paper, we give a detailed overview of the architecture of AIfES and the applied design principles. Finally, we compare AIfES with TensorFlow Lite for Microcontrollers (TFLM) on an ARM Cortex-M4-based System-on-Chip (SoC) using fully connected neural networks (FCNNs) and convolutional neural networks (CNNs). AIfES outperforms TFLM in both execution time and memory consumption for the FCNNs. Additionally, using AIfES reduces memory consumption by up to 54% when using CNNs. Furthermore, we show the performance of AIfES during the training of FCNN as well as CNN and demonstrate the feasibility of training a CNN on a resource-constrained device with a memory usage of slightly more than 100 kB of RAM.

Analysis of the parasitic capacitance effects on the layout of latch-based sense amplifiers for improving SRAM performance

Static random-access memory (SRAM) technology is utilized in designing cache memory to enhance the processing performance of computer systems. The sense amplifier (SA) circuit, a crucial component of memory design, significantly impacts data access time and power consumption. In comparison to conventional differential sense amplifiers (DSA) designs, latch-based sense amplifiers (LSA) used in memory-based computing platforms have specific requirements, including robust noise resistance in harsh working environments and low power consumption, particularly for internet of thing (IoT) embedded computing applications. However, the performance can be degraded due to various factors that arise during the layout, such as conductor resistance or the development of parasitic capacitance. Therefore, this study employs low-voltage 22 nm UMC CMOS technology for LSA design layout and analyzes the factors influencing design performance post-layout process. Layout design optimization techniques are applied to mitigate the impact of parasitic capacitance on important signal lines such as data line/data line bar (DLL/DLLB). Based on the performance analysis results, it is possible to achieve a reduction in power consumption of up to 15% and a 5% decrease in read delay time by implementing circuit layout LSA design optimization techniques.

Cometes: Cross-Device Mapping for Energy and Time-Aware Deployment on Edge Infrastructures

As the Internet of Things (IoT) application development falls more on libraries and tools, software developers design applications targeting power-constrained Edge-computing devices. However, not only the Quality-of-Service (QoS) but also energy savings remain major challenges. This article presents Cometes, a complete tool-flow that extends modern SDK tools, assisting developers in selecting resources towards the desired trade-off between execution time and energy consumption, without exceeding the power limitations of the Edge clusters. Cometes runs at application design time, on top of Kubernetes, and exploits cross-device estimation methods as well as traditional multi-objective optimizers to enable efficient resource allocation. The presented solution achieves up to 66% energy savings, up to 50% speed-up and an average improvement of 7%, compared to the placement policy of the lightweight Kubernetes (k3s).

Few-Shot Medical Image Segmentation via Generating Multiple Representative Descriptors.

Automatic medical image segmentation has witnessed significant development with the success of large models on massive datasets. However, acquiring and annotating vast medical image datasets often proves to be impractical due to the time consumption, specialized expertise requirements, and compliance with patient privacy standards, etc. As a result, Few-shot Medical Image Segmentation (FSMIS) has become an increasingly compelling research direction. Conventional FSMIS methods usually learn prototypes from support images and apply nearest-neighbor searching to segment the query images. However, only a single prototype cannot well represent the distribution of each class, thus leading to restricted performance. To address this problem, we propose to Generate Multiple Representative Descriptors (GMRD), which can comprehensively represent the commonality within the corresponding class distribution. In addition, we design a Multiple Affinity Maps based Prediction (MAMP) module to fuse the multiple affinity maps generated by the aforementioned descriptors. Furthermore, to address intra-class variation and enhance the representativeness of descriptors, we introduce two novel losses. Notably, our model is structured as a dual-path design to achieve a balance between foreground and background differences in medical images. Extensive experiments on four publicly available medical image datasets demonstrate that our method outperforms the state-of-the-art methods, and the detailed analysis also verifies the effectiveness of our designed module.

Experimental and numerical investigation of chemical-loop steam methane reforming on monolithic BaCoO3/CeO2 oxygen

Chemical looping steam methane reforming (CL-SMR) is a promising approach to co-production of syngas and hydrogen. As the CL-SMR process relies on the redox reaction of the oxygen carriers, the reactivity of oxygen carriers is crucial in the performance of CL-SMR. In this study, a monolithic BaCoO3/CeO2 oxygen carrier was synthesized, and the reaction performance was evaluated in CL-SMR. The kinetic parameters were determined based on Sestak-Berggren model, an unsteady-state CFD model was established to predict the dynamic reaction processes in three reaction stages of CL-SMR. The result suggests that the flow rate of inlet gas in reaction stages could affect the mass transfer during gas-solid reactions, and then change the evolution and distribution of reaction rate in monolithic oxygen carrier. Increasing the flow rate could improve the uniformity of reaction rate distribution and decrease the time consumption for cyclic process, but decreased the concentration of gas productions and increased the burden of gas product separation at the same time. Among three reaction stages, the conversion of oxygen carrier was accelerated with the increase of temperature, and the varied half-life time of conversions suggested that the reactivity in RS stage is intense and the reactivity in AC stage is mild.

A machine learning framework for the prediction of antibacterial capacity of silver nanoparticles

The biocompatibility property has made silver nanoparticles powerful candidates for various nanomedical applications. Research interest in silver nanoparticles as a viable alternative to antibiotics is gaining more attention due to their enhanced antimicrobial activity, better antibacterial activity and low cytotoxicity. Machine Learning (ML) has become a state-of-the-art analytic and modelling tool in recent times, due to its prediction capabilities and increased accuracy of the results. In this work, we present machine-learning techniques to predict the antibacterial capacity of silver nanoparticles and extended the work on antifungal studies. In the first phase, we reviewed 50 articles and collected data points for training the model, which consists of features such as core size, shape of the nanoparticle, dosage, bacteria/fungi species and zone of inhibition (ZOI). Then, we trained the data using eight different machine-learning regression algorithms and validated the models’ performance using four metrics such as RMSE, MSE, MAE and R2. Furthermore, the importance of features used in the prediction models has been evaluated. The feature importance revealed that the core size of silver nanoparticles is the prominent feature in the prediction of the antibacterial capacity. The optimum model for the prediction of antibacterial and antifungal activity has been identified. Finally, the model’s validation has also been demonstrated. This work enables researchers to utilize Machine Learning which in turn can address the challenges of time consumption, and cost in laboratory experiments while minimising the reliance on trial and error.

Comparison of the effectiveness of the endodontic treatment using Reciproc Blue instrument with or without glide path preparation

Background: To overcome file fracture due to torsional stress and cyclic fatigue during root canal preparation, the Reciproc Blue endodontic file system with reciprocating motion helps to shape the canal with only one instrument. Objective: To compare the time of root canal shaping and the results of endodontic treatment of first molars with Reciproc Blue files with and without glide path preparation. Materials and Methods: A controlled clinical trial on 78 first molars with irreversible pulpitis in patients visiting Hue University of Medicine and Pharmacy Hospital from July 2020 to October 2022. Group 1 and Group 2 (n = 36 each) were respectively prepared with a Reciproc Blue file with and without a glide path preparation. Comparison of preparation time and treatment outcome based on clinical and radiographic characteristics at 1 week, 3 months, and 6 months. Results: Endodontic treatment using Reciproc Blue file without glide path preparation recorded a shorter time consumption (p < 0.05) and no complications. However, the treatment results after 1 week, 3 months, and 6 months were not different between the two groups regards to glide path preparation (p > 0.05). Conclusion: Shaping root canals with Reciproc Blue without creating a glide path reduces shaping time, simplifies the procedure, and does not adversely affect treatment outcomes compared to the group with glide path creation. Key words: First molar, glide path, Reciproc Blue, root canal preparation.

A simulation approach for determining maximal control cycle time in force-compliant assembly under contact force/torque constraints

Abstract Determining the maximum force feedback control cycle time for an assembly task at a given force/torque threshold is an important basis for designing and evaluating the corresponding force-compliant assembly method. For this issue, this paper proposes a simulation method for determining the maximum force feedback cycle time. The feasible region of the assembly process pose is determined by Monte Carlo simulation. Based on the particle swarm optimization, the pose and motion direction that generate the maximum contact force are found in the feasible region. Finally, dynamic simulation is carried out to determine the maximum force feedback control cycle. Experiments carried out for single-hole assembly show that the proposed approach can effectively determine the maximum force feedback control cycle time, and has a comparative advantage in convergence and total time consumption.

Containment and suppression of compartment fires using specialized liquid compositions

Experimental research findings are reported on the characteristics of physicochemical processes at different stages of fire development and suppression. Fires involving solid materials like wood, cardboard and linoleum were investigated. The minimum necessary and sufficient volumes of firefighting liquid (water with different additives, e.g., a foaming agent, bentonite, bischofite, etc.) and their spraying times were identified. Differences in spraying characteristics (droplet velocities, sizes and concentration) were recorded. A relationship between the spraying characteristics of fire extinguishing agents and efficiency of fire suppression was established. Correlations were identified between the operational characteristics of fire detectors during fire suppression involving several fire extinguishing agents. The most efficient compositions in terms of fire suppression time and extinguishing agent consumption were found for typical indoor combustible materials. Mathematical equations were derived to predict the key characteristics of fire suppression.

Hydrodynamic intensification and interfacial regulation strategy for the mixing process of non-Newtonian fluids

Efficiently realizing laminar flow mixing of non-Newtonian fluids is a key challenge faced by conventional stirred reactors. In this study, an innovative strategy is proposed to regulate the flow pattern from radial to axial flow by changing the interface evolution of the flow field, so as to solve the problem of uneven mixing materials in laminar flow. An innovative combination of the RGB (Red, Green, Blue) brightness analysis and quantitative analysis area method was used to quantitatively describe the mixing performance of the three stirred reactor in the laminar flow. We found that the coloring area ratio of the dual shaft off-centred mixer (DSO) mixer was approximately 165.7 % and 93.8 % higher than that of the single shaft central (SSC) and single shaft off-centred mixer (SSO), respectively. Results showed that the DSO mixer can directionally adjust the stable interface of the flow field, and then obtain the ideal velocity distribution and flow pattern. Importantly, it is found that the mixer has an inherent axial mixing limit in the laminar flow. Increasing the Reynolds number can only shorten the time for the mixing system to reach the steady state, and cannot further improve the axial transport capacity of the system. Compared to the SSC and SSO systems, the DSO mixer demonstrated a reduction of nearly 20 % in overall mixing time and power consumption. Through comparative analysis of pressure distribution and Poincaré cross section, the DSO mixing system can switch chaos oscillation and realize the “globally chaotic mixing” from “locally chaotic mixing”. Remarkably, this work highlights the potential of DSO mixer as a simple and efficient system for laminar flow mixing applications, such as polymerization processes, biological fermentation.

Green Ca-source of cockle shells converted to calcium acetate for environmental sustainability

This work aimed to synthesize and characterize the calcium acetate monohydrate (Ca(CH3COO)2·H2O) from the exothermic reaction between CaCO3 powder derived from cockle shells with three different acetic acids (8, 10, and 12 mol L−1) concentrations by the rapid and easy process without pH and temperature control to lead to cheap chemical production. The physicochemical characteristics of all synthesized Ca(CH3COO)2·H2O samples are investigated based on the chemical compositions, crystal structures, vibrational characteristics, morphologies, and thermal behavior to confirm the target compound. A suitable concentration of 10 mol L−1 CH3COOH was chosen to produce Ca(CH3COO)2·H2O with the highest yield (96.30 %), maximum calcium content (96.2 % CaO) with lower impurities, and time consumption of 17 h. The calcium acetate product obtained from cockle shells in this work shows differences in thermal stability, morphological structure purity, %yield, and metal contamination with those reported obtained from other sources and another shell type in the previous work. This research investigates the transformation of cockle shell waste into CaCO3 for the production of calcium acetate, aiming to address environmental sustainability concerns by reducing the use of calcium ore resources and greenhouse gas emissions.

Predicting the thermoelectric figure of merit in p-type BiSbTe-based alloys using artificial neural network modeling

The polycrystalline Bi0.5Sb1.5Te3 compound has drawn promising thermoelectric material over the decades with a figure of merit (zT) close to unity and numerous experiments have been conducted through a trial-and-error approach to further improve their zT by tuning the chemical composition and processing approaches. Despite a lot of studies available on BiSbTe alloys, choosing the right combination of alloy system with high zT is still a challenging task. Herein, we produced a p-type Bi0.5Sb1.5Te3 alloy through casting followed by high-energy milling to enhance the zT via reducing thermal conductivity. The experimental results revealed that a peak zT of 0.95 was achieved at 300 K due to a substantial reduction in thermal conductivity via enhanced carrier/phonon scattering at refined grain boundaries. In addition to the current scenario, the machine learning (ML) assisted artificial neural network (ANN) modeling was performed to predict the zT value by applying the composition, electrical conductivity, Seebeck coefficient, thermal conductivity, and temperature as the input parameters. The predicted zT by ANN modeling shows excellent accuracy with experimental findings about an average absolute error of 9.34 %. Therefore, the developed ANN modeling shows a protocol for composition-based prediction of zT with reduced experimental costs, time consumption, and excellent accuracy in terms of the theoretical and experimental databases.

Exploring the potential of pretrained CNNs and time-frequency methods for accurate epileptic EEG classification: a comparative study

Prompt diagnosis of epilepsy relies on accurate classification of automated electroencephalogram (EEG) signals. Several approaches have been developed to characterize epileptic EEG data; however, none of them have exploited time-frequency data to evaluate the effect of tweaking parameters in pretrained frameworks for EEG data classification. This study compares the performance of several pretrained convolutional neural networks (CNNs) namely, AlexNet, GoogLeNet, MobileNetV2, ResNet-18 and SqueezeNet for the localization of epilepsy EEG data using various time-frequency data representation algorithms. Continuous wavelet transform (CWT), empirical Fourier decomposition (EFD), empirical mode decomposition (EMD), empirical wavelet transform (EWT), and variational mode decomposition (VMD) were exploited for the acquisition of 2D scalograms from 1D data. The research evaluates the effect of multiple factors, including noisy versus denoised scalograms, different optimizers, learning rates, single versus dual channels, model size, and computational time consumption. The benchmark Bern-Barcelona EEG dataset is used for testing purpose. Results obtained show that the combination of MobileNetV2, Continuous Wavelet Transform (CWT) and Adam optimizer at a learning rate of 10−4, coupled with dual-data channels, provides the best performance metrics. Specifically, these parameters result in optimal sensitivity, specificity, f1-score, and classification accuracy, with respective values of 96.06%, 96.15%, 96.08%, and 96.10%. To further corroborate the efficacy of opted pretrained models on exploited Signal Decomposition (SD) algorithms, the classifiers are also being simulated on Temple University database at pinnacle modeling composition. A similar pattern in the outcome readily validate the findings of our study and robustness of deep learning models on epilepsy EEG scalograms.The conclusions drawn emphasize the potential of pretrained CNN-based models to create a robust, automated system for diagnosing epileptiform. Furthermore, the study offers insights into the effectiveness of varying time-frequency techniques and classifier parameters for classifying epileptic EEG data.